Selecting an Optimized COTS Filter Set for Multispectral Plenoptic Sensing

Timothy Doster, Colin C. Olson, Erin Fleet, Michael Yetzbacher; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 99-107

A 16-band plenoptic camera allows for the rapid exchange of filter sets via a 4x4 filter array on the lens's front aperture thus allowing an operator to quickly adapt to a different locale or threat intelligence. Typically, such a system incorporates a default set of 16 equally spaced, non-overlapping, flat-topped filters. Knowing the operating theater or the likely targets of interest it becomes advantageous to tune the filters; we propose a differential evolution algorithm to search over a set of commercial off-the-shelf (COTS) filters for an optimal solution. We examine two independent tasks: general spectral sensing and target detection. For general spectral sensing, we utilize compressive sensing and find filters that generate codings which minimize reconstruction error. For target detection, we select the filters to optimize the separation between the background and a set of targets. We compare our results to the default filter set and full spectral resolution hyperspectral data.